One problem here is how to find suitable image descriptors for image retrieval.
Deep learning based approaches [1]}, [2]} are based on a pre-trained ConvNet model to extract image-level features, and then use these features to evaluate the similarities against other images. In challenging situations, local descriptors are first extracted, followed by being aggregated to obtain robust global descriptors. A good example is NetVLAD [3]} that designs a trainable generalized VLAD (the Vector of Locally
Aggregated Descriptors) layer. This VLAD layer can be plugged into the off-the-shelf ConvNet architecture to encourage better descriptors learning for image retrieval.
